M.S. AAI Capstone Chronicles 2024

Detecting Fake News Using Natural Language Processing gradient problem. Dropout regularization was strategically chosen to prevent overfitting. Adjustments were made to the embedding dimension and vocabulary size to balance the richness of word representations and computational efficiency. The final step involves the integration of a pre-trained two-layer BI-LSTM deep learning model, with Local Interpretable Model-agnostic Explanations (LIME) for text classification tasks. The model is loaded from a pre-trained h5 file, eliminating the need for training from scratch. Text input undergoes preprocessing, including tokenization, removal of stopwords and punctuation, and stemming, to ensure uniformity in input representation. LIME provides local explanations for model predictions, highlighting the key features contributing to each prediction. The explanations are visualized as HTML content and accompanying images, facilitating human understanding of the model's decision-making process. The Gradio library enables the creation of a user-friendly interface for interactively inputting text and viewing both model predictions and LIME explanations, enhancing interpretability and transparency in the classification process. Results

Table1

Results (Weighted Average) From a Variety of Machine/Deep Learning Methods Used to Detect Fake

News.

Model

Accuracy

Precision Recall

F1-Score

Random Forest

0.94

0.94

0.94

0.94

Distill-BERT

0.95

0.95

0.95

0.95

LSTM

0.94

0.94

0.94

0.94

Stacked LSTM

0.94

0.94

0.94

0.94

Bidirectional LSTM

0.94

0.94

0.94

0.94

Two-layer Bi-LSTM (Best Model)

0.94

0.94

0.94

0.94

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